Map matching algorithms are well known in the industry and used extensively for the land vehicle positioning/navigation systems. First, maps were generated, mainly using aerial images, and commercial navigation system started with GPS accuracy below 100 meter. Then, GPS accuracy became better to below 10 meter, and the map accuracy also increased (in most of road classes) to around 5 meter for absolute accuracy and 1 meter for relative accuracy. In all cases, the resulted map matched position has better lateral accuracy than the stand-alone GPS accuracy.
Now, with the differential correction like the Wide Area Augmentation System (WAAS), the GPS accuracy can go below 1.5 meter. Most of the V2X (vehicle to infrastructure plus vehicle) safety (especially urban road intersection safety) and mobility application requires lane level position accuracy. Automated vehicle in most of its levels requires lane level position accuracy. Therefore, accurate road representation map is very critical in making automated vehicle reality, and getting the maximum benefit from the V2X technology. Thus, matching the vehicle position to the true lane is a key step. However, GPS accuracy can be severely degraded in urban areas especially with tall building and green areas with significant tree existence. In addition, road geometry can be complex and ambiguous, such as road branching. Finally, vehicle dynamics such as lane change and turning can also complicate the map matching problem.
Below, we present a system and method that is capable of dealing with lane level matching problem, and provide different methods of estimating position corrections, position and map matched confidence, and self-correcting map matching (i.e., Map Matching Algorithm (at lane-level)).
One aspect of the present invention relates to or combines with a system that uses the Vehicle to Vehicle (V2V) and/or the Vehicle to infrastructure communication for safety and mobility applications. The invention provides methods and systems to make the V2X realized and effectively used in any intelligent transportation system toward automated vehicle system. This can be used for more safety and/or more automation.
The safety, health, and cost of accidents (on both humans and properties) are major concerns for all citizens, local and Federal governments, cities, insurance companies (both for vehicles and humans), health organizations, and the Congress (especially due to the budget cuts, in every level). People inherently make a lot of mistakes during driving (and cause accidents), due to the lack of sleep, various distractions, talking to others in the vehicle, fast driving, long driving, heavy traffic, rain, snow, fog, ice, or too much drinking. If we can make the driving more automated by implementing different scale of safety applications and even controlling the motion of the vehicle for longer period of driving, that saves many lives and potentially billions of dollars each year, in US and other countries. We introduce here an automated vehicle infrastructure and control systems and methods, and its related technologies.